Methods and apparatus for the planning and delivery of radiation treatments

ABSTRACT

Methods and apparatus are provided for planning and delivering radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. In some embodiments the radiation source is moved continuously along the trajectory while in some embodiments the radiation source is moved intermittently. Some embodiments involve the optimization of the radiation delivery plan to meet various optimization goals while meeting a number of constraints. For each of a number of control points along a trajectory, a radiation delivery plan may comprise: a set of motion axes parameters, a set of beam shape parameters and a beam intensity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/986,420 filed 7 Jan. 2011. U.S. application Ser. No. 12/986,420 is acontinuation of U.S. application Ser. No. 12/132,597 filed 3 Jun. 2008.U.S. application Ser. No. 12/132,597 is a continuation-in-part of U.S.application Ser. No. 11/996,932 which is a 35 USC §371 applicationhaving a 35 USC §371 date of 25 Jan. 2008 and corresponding toPCT/CA2006/001225. PCT/CA2006/001225 has an international filing date of25 Jul. 2006 and claims priority from U.S. patent application No.60/701,974 filed on 25 Jul. 2005.

PCT application No. PCT/CA2006/001225 and U.S. application Ser. Nos.12/986,420, 12/132,597, 11/996,932, and 60/701,974 are herebyincorporated herein by reference.

TECHNICAL FIELD

This invention relates to radiation treatment. The invention relatesparticularly to methods and apparatus for planning and deliveringradiation to a subject to provide a desired three-dimensionaldistribution of radiation dose.

BACKGROUND

The delivery of carefully-planned doses of radiation may be used totreat various medical conditions. For example, radiation treatments areused, often in conjunction with other treatments, in the treatment andcontrol of certain cancers. While it can be beneficial to deliverappropriate amounts of radiation to certain structures or tissues, ingeneral, radiation can harm living tissue. It is desirable to targetradiation on a target volume containing the structures or tissues to beirradiated while minimizing the dose of radiation delivered tosurrounding tissues. Intensity modulated radiation therapy (IMRT) is onemethod that has been used to deliver radiation to target volumes inliving subjects.

IMRT typically involves delivering shaped radiation beams from a fewdifferent directions. The radiation beams are typically delivered insequence. The radiation beams each contribute to the desired dose in thetarget volume.

A typical radiation delivery apparatus has a source of radiation, suchas a linear accelerator, and a rotatable gantry. The gantry can berotated to cause a radiation beam to be incident on a subject fromvarious different angles. The shape of the incident radiation beam canbe modified by a multi-leaf collimator (MLC). A MLC has a number ofleaves which are mostly opaque to radiation. The MLC leaves define anaperture through which radiation can propagate. The positions of theleaves can be adjusted to change the shape of the aperture and tothereby shape the radiation beam that propagates through the MLC. TheMLC may also be rotatable to different angles.

Objectives associated with radiation treatment for a subject typicallyspecify a three-dimensional distribution of radiation dose that it isdesired to deliver to a target region within the subject. The desireddose distribution typically specifies dose values for voxels locatedwithin the target. Ideally, no radiation would be delivered to tissuesoutside of the target region. In practice, however, objectivesassociated with radiation treatment may involve specifying a maximumacceptable dose that may be delivered to tissues outside of the target.

Treatment planning involves identifying an optimal (or at leastacceptable) set of parameters for delivering radiation to a particulartreatment volume. Treatment planning is not a trivial problem. Theproblem that treatment planning seeks to solve involves a wide range ofvariables including:

-   -   the three-dimensional configuration of the treatment volume;    -   the desired dose distribution within the treatment volume;    -   the locations and radiation tolerance of tissues surrounding the        treatment volume; and    -   constraints imposed by the design of the radiation delivery        apparatus.        The possible solutions also involve a large number of variables        including:    -   the number of beam directions to use;    -   the direction of each beam;    -   the shape of each beam; and    -   the amount of radiation delivered in each beam.

Various conventional methods of treatment planning are described in:

-   S. V. Spirou and C.-S. Chui. A gradient inverse planning algorithm    with dose-volume constraints, Med. Phys. 25, 321-333 (1998);-   Q. Wu and R. Mohand. Algorithm and functionality of an intensity    modulated radiotherapy optimization system, Med. Phys. 27, 701-711    (2000);-   S. V. Spirou and C.-S. Chui. Generation of arbitrary intensity    profiles by dynamic jaws or multileaf collimators, Med. Phys. 21,    1031-1041 (1994);-   P. Xia and L. J. Verhey. Multileaf collimator leaf sequencing    algorithm for intensity modulated beams with multiple static    segments, Med. Phys. 25, 1424-1434 (1998); and-   K. Otto and B. G. Clark. Enhancement of IMRT delivery through MLC    rotation,” Phys. Med. Biol. 47, 3997-4017 (2002).

Acquiring sophisticated modern radiation treatment apparatus, such as alinear accelerator, can involve significant capital cost. Therefore itis desirable to make efficient use of such apparatus. All other factorsbeing equal, a radiation treatment plan that permits a desireddistribution of radiation dose to be delivered in a shorter time ispreferable to a radiation treatment plan that requires a longer time todeliver. A treatment plan that can be delivered in a shorter timepermits more efficient use of the radiation treatment apparatus. Ashorter treatment plan also reduces the risk that a subject will moveduring delivery of the radiation in a manner that may significantlyimpact the accuracy of the delivered dose.

Despite the advances that have been made in the field of radiationtherapy, there remains a need for radiation treatment methods andapparatus and radiation treatment planning methods and apparatus thatprovide improved control over the delivery of radiation, especially tocomplicated target volumes. There also remains a need for such methodsand apparatus that can deliver desired dose distributions relativelyquickly.

SUMMARY

One aspect of the invention provides a method for planning delivery ofradiation dose to a target area within a subject. The method comprises:defining a set of one or more optimization goals, the set of one or moreoptimization goals comprising a desired dose distribution in thesubject; specifying an initial plurality of control points along aninitial trajectory, the initial trajectory involving relative movementbetween a radiation source and the subject in a source trajectorydirection; and iteratively optimizing a simulated dose distributionrelative to the set of one or more optimization goals to determine oneor more radiation delivery parameters associated with each of theinitial plurality of control points. For each of the initial pluralityof control points, the one or more radiation delivery parameters maycomprise positions of a plurality of leaves of a multi-leaf collimator(MLC). The MLC leaves may be moveable in a leaf-translation direction.During relative movement between the radiation source and the subjectalong the initial trajectory, the leaf-translation direction is orientedat a MLC orientation angle φ with respect to the source trajectorydirection and wherein an absolute value of the MLC orientation angle φsatisfies 0°<|φ|<90°.

Another aspect of the invention provides a method for deliveringradiation dose to a target area within a subject. The method comprises:defining a trajectory for relative movement between a treatmentradiation source and the subject in a source trajectory direction;determining a radiation delivery plan; and while effecting relativemovement between the treatment radiation source and the subject alongthe trajectory, delivering a treatment radiation beam from the treatmentradiation source to the subject according to the radiation delivery planto impart a dose distribution on the subject. Delivering the treatmentradiation beam from the treatment radiation source to the subjectcomprises varying at least one of: an intensity of the treatmentradiation beam; and a shape of the treatment radiation beam over atleast a portion of the trajectory.

Varying at least one of the intensity of the treatment radiation beamand the shape of the treatment radiation beam over at least the portionof the trajectory, may comprise varying positions of a plurality ofleaves of a multi-leaf collimator (MLC) in a leaf-translation direction.During relative movement between the treatment radiation source and thesubject along the trajectory, the leaf-translation direction may beoriented at a MLC orientation angle φ with respect to the sourcetrajectory direction wherein an absolute value of the MLC orientationangle φ satisfies 0°<|φ|<90°.

Varying at least one of the intensity of the treatment radiation beamand the shape of the treatment radiation beam over at least the portionof the trajectory may comprise varying a rate of radiation output of theradiation source while effecting continuous relative movement betweenthe treatment radiation source and the subject along the trajectory.

Other aspects of the invention provide program products comprisingcomputer readable instructions which, when executed by a processor,cause the processor to execute, at least in part, any of the methodsdescribed herein. Other aspects of the invention provide systemscomprising, inter alia, controllers configured to execute, at least inpart, any of the methods described herein.

Further aspects of the invention and features of embodiments of theinvention are set out below and illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The appended drawings illustrate non-limiting example embodiments of theinvention.

FIG. 1 is a schematic view of an exemplary radiation delivery apparatusin conjunction with which the invention may be practised.

FIG. 1A is a schematic view of another exemplary radiation deliveryapparatus in conjunction with which the invention may be practised.

FIG. 2 is a schematic illustration of a trajectory.

FIG. 3A is a schematic cross-sectional view of a beam-shaping mechanism.

FIG. 3B is a schematic beam's eye plan view of a multi-leafcollimator-type beam-shaping mechanism.

FIG. 3C schematically depicts a system for defining the angle ofleaf-translation directions about the beam axis.

FIG. 4A is a flow chart illustrating a method of optimizing dosedelivery according to a particular embodiment of the invention.

FIG. 4B is a schematic flow chart depicting a method for planning anddelivering radiation to a subject according to a particular embodimentof the invention.

FIGS. 5A, 5B and 5C illustrate dividing an aperture into beamletsaccording to a particular embodiment of the invention.

FIG. 6 graphically depicts the error associated with a dose simulationcalculation versus the number of control points used to perform the dosesimulation calculation.

FIG. 7 graphically depicts dose quality versus the number ofoptimization iterations for several different numbers of control points.

FIG. 8 represents a flow chart which schematically illustrates a methodof optimizing dose delivery according to another embodiment of theinvention where the number of control points is varied over theoptimization process.

FIG. 9 graphically depicts the dose distribution quality versus thenumber of iterations for the FIG. 8 optimization method where the numberof control points is varied over the optimization process.

FIG. 10 is a depiction of sample target tissue and healthy tissue usedin an illustrative example of an implementation of a particularembodiment of the invention.

FIGS. 11A and 11B respectively depict the initial control pointpositions of the motion axes corresponding to a trajectory used in theFIG. 10 example.

FIGS. 12A-12F depict a dose volume histogram (DVH) which isrepresentative of the dose distribution quality at various stages of theoptimization process of the FIG. 10 example.

FIG. 13 another graphical depiction of the optimization process of theFIG. 10 example.

FIGS. 14A-14D show the results (the motion axes parameters, theintensity and the beam shaping parameters) of the optimization processof the FIG. 10 example.

FIG. 15 plots contour lines of constant dose (isodose lines) in atwo-dimensional cross-sectional slice of the target region in the FIG.10 example.

FIGS. 16A and 16B show examples of how the selection of a particularconstant MLC orientation angle may impact treatment plan quality andultimately the radiation dose that is delivered to a subject.

FIG. 17 schematically depicts how target and healthy tissue will lookfor opposing beam directions.

FIGS. 18A and 18B show an MLC and the respective projections of a targetand healthy tissue for opposing beam directions corresponding toopposing gantry angles.

FIGS. 18C and 18D show an MLC and the respective projections of adesired beam shape for opposing beam directions corresponding toopposing gantry angles.

DESCRIPTION

Throughout the following description specific details are set forth inorder to provide a more thorough understanding to persons skilled in theart. However, well known elements may not have been shown or describedin detail to avoid unnecessarily obscuring the disclosure. Accordingly,the description and drawings are to be regarded in an illustrative,rather than a restrictive, sense.

This invention relates to the planning and delivery of radiationtreatments by modalities which involve moving a radiation source along atrajectory relative to a subject while delivering radiation to thesubject. In some embodiments the radiation source is moved continuouslyalong the trajectory while in some embodiments the radiation source ismoved intermittently. Some embodiments involve the optimization of theradiation delivery plan to meet various optimization goals while meetinga number of constraints. For each of a number of control points along atrajectory, a radiation delivery plan may comprise: a set of motion axesparameters, a set of beam shape parameters and a beam intensity.

FIG. 1 shows an example radiation delivery apparatus 10 comprising aradiation source 12 capable of generating or otherwise emitting a beam14 of radiation. Radiation source 12 may comprise a linear accelerator,for example. A subject S is positioned on a table or “couch” 15 whichcan be placed in the path of beam 14. Apparatus 10 has a number ofmovable parts that permit the location of radiation source 12 andorientation of radiation beam 14 to be moved relative to subject S.These parts may be referred to collectively as a beam positioningmechanism 13.

In the illustrated radiation delivery apparatus 10, beam positioningmechanism 13 comprises a gantry 16 which supports radiation source 12and which can be rotated about an axis 18. Axis 18 and beam 14 intersectat an isocenter 20. Beam positioning mechanism 13 also comprises amoveable couch 15. In exemplary radiation delivery apparatus 10, couch15 can be translated in any of three orthogonal directions (shown inFIG. 1 as X, Y, and Z directions) and can be rotated about an axis 22.In some embodiments, couch 15 can be rotated about one or more of itsother axes. The location of source 12 and the orientation of beam 14 canbe changed (relative to subject S) by moving one or more of the movableparts of beam positioning mechanism 13.

Each separately-controllable means for moving source 12 and/or orientingbeam 14 relative to subject S may be termed a “motion axis”. In somecases, moving source 12 or beam 14 along a particular trajectory mayrequire motions of two or more motion axes. In exemplary radiationdelivery apparatus 10, motion axes include:

-   -   rotation of gantry 16 about axis 18;    -   translation of couch 15 in any one or more of the X, Y, Z        directions; and    -   rotation of couch 15 about axis 22.

Radiation delivery apparatus 10 typically comprises a control system 23capable of controlling, among other things, the movement of its motionaxes and the intensity of radiation source 12. Control system 23 maygenerally comprise hardware components and/or software components. Inthe illustrated embodiment, control system 23 comprises a controller 24capable of executing software instructions. Control system 23 ispreferably capable of receiving (as input) a set of desired positionsfor its motion axes and, responsive to such input, controllably movingone or more of its motion axes to achieve the set of desired motion axespositions. At the same time, control system 23 may also control theintensity of radiation source 12 in response to input of a set ofdesired radiation intensities.

While radiation delivery apparatus 10 represents a particular type ofradiation delivery apparatus in conjunction with which the invention maybe implemented, it should be understood that the invention may beimplemented on different radiation delivery apparatus which may comprisedifferent motion axes. In general, the invention may be implemented inconjunction with any set of motion axes that can create relativemovement between a radiation source 12 and a subject S, from a startingpoint along a trajectory to an ending point.

Another example of a radiation delivery apparatus 10A that provides analternative set of motion axes is shown in FIG. 1A. In exemplaryapparatus 10A, source 12 is disposed in a toroidal housing 26. Amechanism 27 permits source 12 to be moved around housing 26 toirradiate a subject S from different sides. Subject S is on a table 28which can be advanced through a central aperture 29 in housing 26.Apparatus having configurations like that shown schematically in FIG. 1Aare used to deliver radiation in a manner commonly called “Tomotherapy”.

In accordance with particular embodiments of the invention, beampositioning mechanism 13 causes source 12 and/or beam 14 to move along atrajectory while radiation dose is controllably delivered to targetregions within subject S. A “trajectory” is a set of one or moremovements of one or more of the movable parts of beam position mechanism13 that results in the beam position and orientation changing from afirst position and orientation to a second position and orientation. Thefirst and second positions and the first and second orientations are notnecessarily different. For example, a trajectory may be specified to bea rotation of gantry 16 from a starting point through an angle of 360°about axis 18 to an ending point in which case the beam position andorientation at the starting and ending points are the same.

The first and second beam positions and beam orientations may bespecified by a first set of motion axis positions (corresponding to thefirst beam position and the first beam orientation) and a second set ofmotion axis positions (corresponding to the second beam position and thesecond beam orientation). As discussed above, control system 23 ofradiation delivery apparatus 10 can controllably move its motion axesbetween the first set of motion axis positions and the second set ofmotion axis positions. In general, a trajectory may be described by morethan two beam positions and beam orientations. For example, a trajectorymay be specified by a plurality of sets of motion axis positions, eachset of motion axis positions corresponding to a particular beam positionand a particular beam orientation. Control system 23 can thencontrollably move its motion axes between each set of motion axispositions.

In general, a trajectory may be arbitrary and is only limited by theparticular radiation delivery apparatus and its particular beampositioning mechanism. Within constraints imposed by the design of aparticular radiation delivery apparatus 10 and its beam positioningmechanism 13, source 12 and/or beam 14 may be caused to follow anarbitrary trajectory relative to subject S by causing appropriatecombinations of movements of the available motion axes. A trajectory maybe specified to achieve a variety of treatment objectives. For example,a trajectory may be selected to have a high ratio of target tissuewithin the beam's eye view compared to healthy tissue within the beam'seye view or to avoid important healthy organs or the like.

For the purpose of implementing the present invention, it is useful todiscretize a desired trajectory into a number of “control points” atvarious locations along the trajectory. A set of motion axis positionscan be associated with each such control point. A desired trajectory maydefine a set of available control points. One way to specify atrajectory of radiation source 12 and/or beam 14 is to specify at a setof discrete control points at which the position of each motion axis isdefined.

FIG. 2 schematically depicts a radiation source 12 travelling relativeto a subject S along an arbitrary trajectory 30 in three-dimensionswhile delivering radiation dose to a subject S by way of a radiationbeam 14. The position and orientation of radiation beam 14 changes assource 12 moves along trajectory 30. In some embodiments, the changes inposition and/or direction of beam 14 may occur substantiallycontinuously as source 12 moves along trajectory 30. While source 12 ismoving along trajectory 30, radiation dose may be provided to subject Scontinuously (i.e. at all times during the movement of source 12 alongtrajectory 30) or intermittently (i.e. radiation may be blocked orturned off at some times during the movement of source 12 alongtrajectory 30). Source 12 may move continuously along trajectory 30 ormay move intermittently between various positions on trajectory 30. FIG.2 schematically depicts a number of control points 32 along trajectory30. In some embodiments, the specification of trajectory 30 defines theset of available control points 32. In other embodiments, the set ofcontrol points 32 are used to define trajectory 30. In such embodiments,the portions of trajectory 30 between control points 32 may bedetermined (e.g. by control system 23) from control points 32 by asuitable algorithm.

In general, control points 32 may be specified anywhere along trajectory30, although it is preferable that there is a control point at the startof trajectory 30, a control point at the end of trajectory 30 and thatthe control points 32 are otherwise spaced-apart along trajectory 30. Insome embodiments of the invention, control points 32 are selected suchthat the magnitudes of the changes in the position of a motion axis overa trajectory 30 are equal as between control points 32. For example,where a trajectory 30 is defined as a 360° arc of gantry 16 about axis18 and where the number of control points 32 along trajectory 30 is 21,then control points 32 may be selected to correspond to 0° (a startingcontrol point), 360° (an ending control point) and 19 other controlpoints at 18° intervals along the arc of gantry 16.

Although trajectory 30 may be defined arbitrarily, it is preferable thatsource 12 and/or beam 14 not have to move back and forth along the samepath. Accordingly, in some embodiments, trajectory 30 is specified suchthat it does not overlap itself (except possibly at the beginning andend of trajectory 30). In such embodiments, the positions of the motionaxes of the radiation delivery apparatus are not the same exceptpossibly at the beginning and end of trajectory 30. In such embodiments,treatment time can be minimized (or at least reduced) by irradiatingsubject S only once from each set of motion axis positions.

In some embodiments, trajectory 30 is selected such that the motion axesof the radiation delivery device move in one direction without having toreverse directions (i.e. without source 12 and/or beam 14 having to bemoved back and forth along the same path). Selection of a trajectory 30involving movement of the motion axes in a single direction can minimizewear on the components of a radiation delivery apparatus. For example,in apparatus 10, it is preferable to move gantry 16 in one direction,because gantry 16 may be relatively massive (e.g. greater than 1 ton)and reversing the motion of gantry 16 at various locations over atrajectory may cause strain on the components of radiation deliveryapparatus 16 (e.g. on the drive train associated with the motion ofgantry 16).

In some embodiments, trajectory 30 is selected such that the motion axesof the radiation delivery apparatus move substantially continuously(i.e. without stopping). Substantially continuous movement of the motionaxes over a trajectory 30 is typically preferable to discontinuousmovement, because stopping and starting motion axes can cause wear onthe components of a radiation delivery apparatus. In other embodiments,the motion axes of a radiation delivery apparatus are permitted to stopat one or more locations along trajectory 30. Multiple control points 32may be provided at such locations to allow the beam shape and/or beamintensity to be varied while the position and orientation of the beam ismaintained constant.

In some embodiments, trajectory 30 comprises a single, one-way,continuous 360° rotation of gantry 16 about axis 18 such that trajectory30 only possibly overlaps itself at its beginning and end points. Insome embodiments, this single, one-way, continuous 360° rotation ofgantry 16 about axis 18 is coupled with corresponding one-way,continuous translational or rotational movement of couch 15, such thattrajectory 30 is completely non-overlapping.

Some embodiments involve trajectories 30 which are effected by anycombination of motion axes of radiation delivery apparatus 10 such thatrelative movement between source 12 and/or beam 13 and subject Scomprises a discrete plurality of arcs, wherein each arc is confined toa corresponding plane (e.g. a rotation of up to 360° of gantry 16 aboutaxis 18). In some embodiments, each arc may be non-self overlapping. Insome embodiments, each arc may overlap only at its beginning and endpoints. In the course of following such a trajectory 30, the motion axesof radiation delivery apparatus 10 may be moved between individual arcssuch that the corresponding planes to which the arcs are confinedintersect with one another (e.g. by suitable rotation of couch 15 aboutaxis 22). Alternatively, the motion axes of radiation delivery apparatus10 may be moved between individual arcs such that the correspondingplanes to which the arcs are defined are parallel with one another (e.g.by suitable translational movement of couch 15). In some cases,radiation may not be delivered to subject S when the motion axes ofradiation delivery apparatus 10 are moved between individual arcs.

Radiation delivery apparatus, such as exemplary apparatus 10 (FIG. 1)and 10A (FIG. 1A), typically include adjustable beam-shaping mechanisms33 located between source 12 and subject S for shaping radiation beam14. FIG. 3A schematically depicts a beam-shaping mechanism 33 locatedbetween source 12 and subject S. Beam-shaping mechanism 33 may comprisestationary and/or movable metal components 31. Components 31 may definean aperture 31A through which portions of radiation beam 14 can pass.Aperture 31A of beam-shaping mechanism 33 may define a two-dimensionalborder of radiation beam 14. In particular embodiments, beam shapingmechanism 33 is located and/or shaped such that aperture 31A is in aplane orthogonal to the direction of radiation from source 12 to thetarget volume in subject S. Control system 23 is preferably capable ofcontrolling the configuration of beam-shaping mechanism 33.

One non-limiting example of an adjustable beam-shaping mechanism 33comprises a multi-leaf collimator (MLC) 35 located between source 12 andsubject S. FIG. 3B schematically depicts a suitable MLC 35. As shown inFIG. 3B, MLC 35 comprises a number of leaves 36 that can beindependently translated into or out of the radiation field to defineone or more apertures 38 through which radiation can pass. Leaves 36,which may comprise metal components, function to block radiation. In theillustrated embodiment, leaves 36 are translatable in theleaf-translation directions indicated by double-headed arrow 41.Leaf-translation directions 41 may be located in a plane that isorthogonal to beam axis 37 (i.e. a direction of the radiation beam 14from source 12 to the target volume in subject S). In the FIG. 3B view,beam axis 37 extends into and out of the page. The size(s) and shape(s)of aperture(s) 38 may be adjusted by selectively positioning each leaf36.

As shown in the illustrate embodiment of FIG. 3B, leaves 36 aretypically provided in opposing pairs. MLC 35 may be mounted so that itcan be rotated to different orientations about beam axis 37—i.e. suchthat leaf-translation directions 41 and the direction of movement ofleaves 36 may be pivoted about beam axis 37. Dotted outline 39 of FIG.3B shows an example of an alternate orientation of MLC 35 wherein MLC 35has been rotated about beam axis 37 such that leaf-translationdirections 41 are oriented at an angle that is approximately 45° fromthe orientation shown in the main FIG. 3B illustration.

It will be appreciated that the angle φ of leaf-translation directions41 about beam axis 37 may be defined relative to an arbitrary referenceaxis. FIG. 3C schematically depicts a system for defining the angle φ ofleaf-translation directions 41 about beam axis 37. In the FIG. 3C, theangle φ of leaf-translation directions 41 about beam axis 37 is definedto be an angle in a range of −90°<φ<=90° relative to a reference axis43. FIG. 3C illustrates a first leaf-translation direction 41A whereinthe angle φ_(A) is greater than zero and a second leaf-translationdirection 41B wherein the angle φ_(B) is less than zero. The angle φ ofleaf-translation directions 41 about beam axis 37 (as defined relativeto reference axis 43 in the above-described manner) may be referred toas the MLC orientation angle φ. In particular embodiments, the referenceaxis 43 may be selected to coincide with the direction of motion of beamaxis 37 as beam positioning mechanism 13 moves source 12 and/or beam 14relative to subject S along trajectory 30. Reference axis 43 maytherefore be referred to herein as source trajectory direction 43.

A configuration of MLC 35 can be specified by a set of leaf positionsthat define a position of each leaf 36 and an MLC orientation angle φ ofMLC 35 about beam axis 37. The control system of a radiation deliverydevice (e.g. control system 23 of radiation delivery device 10) istypically capable of controlling the positions of leaves 36 and the MLCorientation angle φ. MLCs can differ in design details, such as thenumber of leaves 36, the widths of leaves 36, the shapes of the ends andedges of leaves 36, the range of positions that any leaf 36 can have,constraints on the position of one leaf 36 imposed by the positions ofother leaves 36, the mechanical design of the MLC, and the like. Theinvention described herein should be understood to accommodate any typeof configurable beam-shaping apparatus 33 including MLCs having theseand other design variations.

The configuration of MLC 35 may be changed (for example, by movingleaves 36 and/or rotating the MLC orientation angle φ of MLC 35 aboutbeam axis 37) while radiation source 12 is operating and while radiationsource 12 is moving about trajectory 30, thereby allowing the shape ofaperture(s) 38 to be varied dynamically while radiation is beingdelivered to a target volume in subject S. Since MLC 35 can have a largenumber of leaves 36, each of leaves 36 can be placed in a large numberof positions and MLC 35 can be rotated about beam axis 37, MLC 35 mayhave a very large number of possible configurations.

FIG. 4A schematically depicts a method 50 according to an exampleembodiment of this invention. An objective of method 50 is to establisha radiation treatment plan that will deliver a desired radiation dosedistribution to a target volume in a subject S (to within an acceptabletolerance), while minimizing the dose of radiation delivered to tissuessurrounding the target volume or at least keeping the dose delivered tosurrounding tissues below an acceptable threshold. This objective may beachieved by varying: (i) a cross-sectional shape of a radiation beam(e.g. beam 14); and (ii) an intensity of the radiation beam, whilemoving radiation source 12 and/or beam 14 along a trajectory 30 relativeto subject S. In some embodiments, as discussed above, these objectivesare achieved while radiation source 12 and/or beam 14 are caused to movecontinuously along trajectory 30.

Method 50 may be performed, at least in part, by a treatment planningsystem 25 (e.g. treatment planning system 25 of FIG. 1). In theillustrated embodiment, treatment planning system 25 comprises its owncontroller 25A which is configured to execute suitable software 25B. Inother embodiments, control system 23 and treatment planning system 25may share a controller. Controller 25 may comprise one or more dataprocessors, together with suitable hardware, including, by way ofnon-limiting example: accessible memory, logic circuitry, drivers,amplifiers, A/D and D/A converters and like. Such a controller maycomprise, without limitation, a microprocessor, a computer-on-a-chip,the CPU of a computer or any other suitable microcontroller. Controller25 may comprise a plurality of data processors.

A desired amount of radiation dose to be delivered to the target volume(referred to as the “desired dose distribution”) and a suitabletrajectory 30 may be defined in advance. Method 50 derives the shapethat beam 14 ought to have during movement of source 12 and/or beam 14along trajectory 30 and the intensity with which radiation ought to bedelivered during movement of source 12 and/or beam 14 along trajectory30. The shape of beam 14 may be determined by a suitable configurationof a beam-shaping mechanism 33, such as MLC 35.

In block 52, method 50 obtains a set of optimization goals 61 andtrajectory data 62 defining a desired trajectory 30. Optimization goals61 comprise dose distribution data 60, which defines a desired dosedistribution, and may comprise other optimization goals 63. Optimizationgoals 61 and/or trajectory data 62 may have been developed by healthprofessionals, such as a radiation oncologist in consultation with aradiation physicist, for example. Optimization goals 61 and/ortrajectory data 62 may be specified by an operator as a part of block52.

The person or persons who develop trajectory 30 may have reference tofactors such as:

-   -   the condition to be treated;    -   the shape, size and location of the target volume;    -   the locations of critical structures that should be spared; and    -   other appropriate factors.        Trajectory 30 may be selected to minimize treatment time.

Radiation delivery apparatus according to some embodiments of theinvention may provide one or more pre-defined trajectories. For example,in some embodiments, a pre-defined trajectory 30 may comprise a single,one-way, continuous 360° rotation of gantry 16 about axis 18 such thattrajectory 30 overlaps itself only at its beginning and end points. Insuch cases, block 52 may comprise selecting a pre-defined trajectory 30or a template that partially defines a trajectory 30 and can becompleted to fully define the trajectory 30.

As discussed above, optimization goals 61 comprise dose distributiondata 60 and may comprise other optimization goals 63. Other optimizationgoals 63 may be specified by an operator as a part of block 52. By wayof non-limiting example, other optimization goals 63 may comprise adesired uniformity of dose distribution in the target volume (or adesired precision with which the dose distribution in the target volumeshould match desired dose distribution data 60). Other optimizationgoals 63 may also define volumes occupied by important structuresoutside of the target volume and set limits on the radiation doses to bedelivered to those structures. Other optimization goals 63 may define amaximum time required to deliver the radiation based on an individualpatient's ability to stay still during treatment. For example, a childmay be more likely to move during treatment than an adult and suchmovement may cause incorrect dose delivery. Consequently, it may bedesirable to lower the maximum dose delivery time for the child tominimize the risk that the child may move during treatment. Otheroptimization goals 63 may also set priorities (weights) for differentoptimization goals.

Other optimization goals 63 may have any of a variety of differentforms. For example, a biological model may be used in the computation ofa metric which estimates a probability that a specified dosedistribution will control a disease from which the subject is sufferingand/or the probability that a specified dose delivered to non-diseasedtissue may cause complications. Such biological models are known asradiobiological models. Other optimization goals 63 may be based in parton one or more radiobiological models. The physical limitations of aparticular radiation delivery apparatus may also be taken into accountas another example of an optimization goal 63. As mentioned above,gantry 12 can be relatively massive and controlled movement of gantry 12may be difficult and may cause strain to various components of theradiation delivery apparatus. As a particular example, one optimizationgoal 63 may be to have gantry 16 move continuously (i.e. withoutstopping) over the specified trajectory 30.

Method 50 then proceeds to an optimization process 54, which seeksdesirable beam shapes and intensities as a function of the position ofsource 12 and/or beam 14 along trajectory 30. In the illustratedembodiment of method 50, optimization process 54 involves iterativelyselecting and modifying one or more optimization variables affecting thebeam shape or the beam intensity. For example, the optimizationvariable(s) may comprise a position of a leaf 36 in a MLC 35 at acontrol point 32 (which determines a shape of beam 14), a MLCorientation angle φ of MLC 35 about axis 37 at a control point 32 (whichdetermines a shape of beam 14) and/or an intensity of beam 14 at acontrol point 32. The quality of the dose distribution resulting fromthe modified optimization variable(s) is evaluated in relation to a setof one or more optimization goals. The modification is then accepted orrejected. Optimization process 54 continues until it achieves anacceptable set of beam shapes and intensities or fails.

In the illustrated method 50, optimization process 54 begins in block 56by establishing an optimization function. The block 56 optimizationfunction is based, at least in part, on optimization goals 61. The setof optimization goals 61 includes the desired dose distribution data 60and may include one or more other optimization goals 63. The block 56optimization function may comprise a cost function. Higher costs(corresponding to circumstances which are farther from optimizationgoals 61) may be associated with factors such as:

-   -   deviations from the desired dose distribution data 60;    -   increases in the radiation dose delivered outside of the target        volume;    -   increases in the radiation dose delivered to critical structures        outside of the treatment volume;    -   increases in the time required to deliver the radiation        treatment; and/or    -   increases in the total radiation output required for the        delivery of the treatment.        Lower costs (corresponding to circumstances which are closer to        optimization goals 61) may be associated with factors such as:    -   radiation doses that come closer to matching specified        thresholds (which may be related to desired dose distribution        data 60);    -   no radiation doses exceeding specified thresholds;    -   reductions in radiation dose outside of the target volume;    -   reductions in radiation dose delivered to critical structures        outside of the target volume;    -   decreases in the time required to deliver the radiation        treatment; and/or    -   decreases in the total radiation output required for the        delivery of the treatment.        These factors may be weighted differently from one another.        Other factors may also be taken into account when establishing        the block 56 optimization function.

The result of block 56 is an optimization function which takes as inputa dose distribution and produces an output having a value or values thatindicate how closely the input dose distribution satisfies a set ofoptimization goals 61.

Block 58 involves initializing beam shapes and intensities for a numberof control points 32 along trajectory 30. The initial beam shapes andintensities may be selected using any of a wide variety of techniques.Initial beam shapes may be selected by specifying a particularconfiguration of MLC 35. By way of non-limiting example, initial beamshapes specified in block 58 may be selected by any of:

-   -   setting the beam shape at each control point 32 along trajectory        30 to approximate a beam's eye view outline of the target volume        (taken from control point 32);    -   setting the beam shape so that radiation is blocked from healthy        tissue structures only;    -   initializing leaves 36 of MLC to be in a specified configuration        such as fully open, fully closed, half-open, or defining a shape        for aperture 38 (e.g. round, elliptical, rectangular or the        like); and    -   randomizing the positions of leaves 36 of MLC.        The particular way that the beam shapes are initialized is not        critical and is limited only by the beam-shaping mechanism 33 of        particular radiation delivery apparatus.

By way of non-limiting example, the initial beam intensities specifiedin block 58 may be selected by any of:

-   -   setting all intensities to zero;    -   setting all intensities to the same value; and    -   setting intensities to random values.

In some embodiments, the beam shapes are initialized in block 58 toshapes that match a projection of the target (e.g. to approximate abeam's eye view outline of the target volume from each control point 32along trajectory 30) and the intensities are initialized in block 58 toall have the same value which may be set so that the mean dose in thetarget volume will equal a prescribed dose.

In block 64, method 50 involves simulating the dose distributionresulting from the initial beam shapes and initial beam intensities.Typically, the block 64 simulation comprises a simulated dosedistribution computation which is discussed in more detail below. Method50 then determines an initial optimization result in block 65. The block65 determination of the initial optimization result may compriseevaluating the block 56 optimization function on the basis of the block64 simulated dose distribution.

In block 66, method 50 alters the beam shapes and/or intensities at oneor more control points 32. The block 66 alteration of beam shapes and/orintensities may be quasi-random. The block 66 alteration of beam shapesand/or intensities may be subject to constraints. For example, suchconstraints may prohibit impossible beam shapes and/or intensities andmay set other restrictions on beam shapes, beam intensities and/or therate of change of beam shapes and/or beam intensities. In each executionof block 66, the alteration of beam shapes and/or intensities mayinvolve a single parameter variation or multiple parameter variations tobeam shape parameter(s) and/or to beam intensity parameter(s). The block66 alteration of beam shapes and/or intensities and may involvevariation(s) of these parameter(s) at a single control point 32 or atmultiple control points 32. Block 68 involves simulating a dosedistribution that would be achieved if the block 66 altered beam shapesand/or intensities were used to provide a radiation treatment.Typically, the block 68 simulation comprises a simulated dosedistribution computation which is discussed in more detail below.

In some embodiments, the block 66 alteration of beam shapes and/orintensities is not chosen randomly, but rather is selected to givepriority to certain parameter(s) that have large impacts on dosedistribution quality. “Dose distribution quality” may comprise areflection of how closely a simulated dose distribution calculationmeets optimization goals 61. For example, where the beam is shaped by aMLC 35, certain leaves 36 or positions of leaves 36 may be givenpriority for modification. This may be done by determining a prioriwhich leaves of MLC 35 have the most impact on dose distributionquality. Such an a priori determination of particularly important MLCleaves may be based, for example, on a calculation of the relativecontributions to the block 56 optimization function from each voxel inthe target region and the surrounding tissue and by a projection of beamray lines intersecting a particular voxel to the plane of MLC 35.

In block 70, method 50 determines a current optimization result. Theblock 70 determination may comprise evaluating the block 56 optimizationfunction on the basis of the block 68 simulated dose distribution. Inblock 72, the current optimization result (determined in block 70) iscompared to a previous optimization result and a decision is madewhether to keep or discard the block 66 alteration. The first time thatmethod 50 arrives at block 72, the previous optimization result may bethe block 65 initial optimization result. The block 72 decision mayinvolve:

-   -   (i) deciding to preserve the block 66 alteration (block 72 YES        output) if the current optimization result is closer to        optimization goals 61 than the previous optimization result; or    -   (ii) deciding to reject the block 66 alteration (block 72 NO        output) if the current optimization result is further from        optimization goals 61 than the previous optimization result.        Other optimization algorithms may make the block 72 decision as        to whether to keep or discard the block 66 alteration based on        rules associated with the particular optimization algorithm. For        example, such optimization algorithms may, in some instances,        allow preservation of the block 66 alteration (block 72 YES        output) if the current optimization result is further from the        optimization goals 61 than the previous optimization result.        Simulated annealing is an example of such an optimization        algorithm.

If block 72 determines that the block 66 alteration should be preserved(block 72 YES output), then method 50 proceeds to block 73, where theblock 66 altered beam shapes and intensities are updated to be thecurrent beam shapes and intensities. After updating the beam shapes andintensities in block 73, method 50 proceeds to block 74. If block 72determines that the block 66 alteration should be rejected (block 72 NOoutput), then method 50 proceeds directly to block 74 (i.e. withoutadopting the block 66 alterations).

Block 74 involves a determination of whether applicable terminationcriteria have been met. If the termination criteria have been met (block74 YES output), method 50 proceeds to block 75, where the current beamshapes and intensities are saved as an optimization result. After block75, optimization process 54 terminates. On the other hand, if thetermination criteria have not been met (block 74 NO output), method 50loops back to perform another iteration of blocks 66 through 74.

By way of non-limiting example, block 74 termination criteria mayinclude any one or more of:

-   -   successful achievement of optimization goals 61;    -   successive iterations not yielding optimization results that        approach optimization goals 61;    -   number of successful iterations of blocks 66 through 74 (where a        successful iteration is an iteration where the block 66        variation is kept in block 73 (i.e. block 72 YES output));    -   operator termination of the optimization process.

The illustrated method 50 represents a very simple optimization process54. Optimization process 54 may additionally or alternatively includeother known optimization techniques such as:

-   -   simulated annealing;    -   gradient-based techniques;    -   genetic algorithms;    -   applying neural networks; or    -   the like.

Method 50 may be used as a part of an overall method for planning anddelivering radiation dose to a subject S. FIG. 4B schematically depictsa method 300 for planning and delivering radiation dose to a subject Saccording to a particular embodiment of the invention. Method 300 beginsin block 310, which, in the illustrated embodiment, involves obtaining adesired trajectory 30 and desired optimization goals 61. Method 300 thenproceeds to block 320 which involves optimizing a set of radiationdelivery parameters. In one particular embodiment, the block 320optimization process may comprise an optimization of the beam shape andbeam intensity parameters in accordance with optimization process 54 ofmethod 50. The result of the block 320 optimization process is aradiation delivery plan. In block 330, the radiation delivery plan isprovided to the control system of a radiation delivery apparatus (e.g.control system 23 of radiation delivery device 10 (FIG. 1)). In block340, the radiation delivery apparatus delivers the radiation to asubject in accordance with the radiation treatment plan developed inblock 320.

Method 50 involves the simulation of dose distribution that results froma particular set of beam shapes, beam intensities and motion axispositions (e.g. in blocks 64 and 68). Simulation of the dosedistribution may be performed in any suitable manner. Some examples ofdose calculation methods that may be employed to simulate dosedistribution results comprise:

-   -   pencil beam superposition;    -   collapsed cone convolution; and    -   Monte Carlo simulation.

In some embodiments, the dose that would be delivered by a treatmentplan is simulated (as in blocks 64 and 68 of method 50) by adding acontribution to the dose from each control point 32. At each of controlpoints 32, the following information is known:

-   -   a position of source 12 and an orientation of beam 14 relative        to subject S including the target volume (as determined by the        positions of the available motion axes);    -   a beam shape (as determined, for example, by a MLC orientation        angle φ and/or a configuration of the leaves 36 of a MLC 35);        and    -   a beam intensity.

In some embodiments, the contribution to the dose at each control point32 is determined by pencil beam superposition. Pencil beam superpositioninvolves conceptually dividing the projected area of beam 14 into manysmall beams known as “beamlets” or “pencil beams”. This may be done bydividing a cross-sectional beam shape (e.g. aperture 38 of MLC 35) intoa grid of square beamlets. The contribution to an overall dosedistribution from a particular control point 32 may be determined bysumming the contributions of the beamlets. The contribution to a dosedistribution by individual beamlets may be computed in advance. Suchcontributions typically take into account radiation scattering and othereffects that can result in the radiation from one beamlet contributingto dose in regions that are outside of the beamlet. In a typical MLC 35,there is some transmission of radiation through leaves 36. Consequently,when performing a dose simulation calculation, it is often desirable addsome smaller contribution to the dose from outside of the beam shapingaperture 38 to account for transmission through leaves 36 of MLC 35.

FIG. 5A shows an aperture 38 of an MLC 35 divided into a plurality ofbeamlets 80. In general, it is desirable for beamlets 80 to be fairlysmall to permit precise modelling of the wide range of configurationsthat aperture 38 may have. Beamlets 80 may be smaller than the widths ofthe leaves 36 (not shown in FIG. 5A) of MLC 35. In FIG. 5A, 105 beamlets80 are required to cover aperture 38 and, consequently, for a particularcontrol point 32 having the aperture configuration shown in FIG. 5A, adose simulation calculation (e.g. a portion of the block 68 dosesimulation) involves a superposition of the dose contributed by 105beamlets 80.

Some embodiments achieve efficiencies in this dose simulationcomputation by providing composite beamlets 82 that are larger thanbeamlets 80. A range of composite beamlets 82 having different sizes,shapes and/or orientations may be provided. FIG. 5B shows a number ofcomposite beamlets 82A, 82B, 82C (collectively, beamlets 82) havingdifferent sizes and shapes. It can be seen from FIG. 5B, that compositebeamlets 82 can be used in the place of a plurality of conventionallysized beamlets 80. An example application of composite beamlets 82 isshown in FIG. 5C. For a given shape of aperture 38, composite beamlets82 are used in place of some or all of smaller beamlets 80. In theparticular configuration of aperture 38 of FIG. 5C (which is the same asthe configuration of aperture 38 of FIG. 5A), the area of aperture 38 iscovered by 28 composite beamlets 82 (24 82A, one 84B, three 84C) and onesmaller beamlet 80. Consequently, for a particular control point 32having the aperture configuration of FIG. 5B, a dose simulationcalculation (e.g. a portion of the block 68 dose simulation) is reducedto a superposition of the dose contributed by 29 beamlets 82, 80. Dosecontributed by composite beamlets 82 may be determined in advance in amanner similar to the advance dose contribution from conventionalbeamlets 80.

The size and shape of composite beamlets 82 may be selected to reduce,and preferably minimize, the number of beamlets required to cover thearea of aperture 38. This can significantly reduce calculation timewithout significantly reducing the accuracy of dose simulation. The useof composite beamlets is not limited to pencil beam superposition andmay be used in other dose simulation calculation algorithms, such asMonte Carlo dose simulation and collapsed cone convolution dosesimulation, for example.

The use of composite beamlets 82 to perform a dose simulationcalculation assumes that there are only small changes in thecharacteristics of the tissue over the cross-sectional dimension of thecomposite beamlet 82. As composite beamlets are made larger, thisassumption may not necessarily hold. Accordingly, the upper size limitof composite beamlets 82 is limited by the necessary calculationaccuracy. In some embodiments, at least one dimension of compositebeamlets 82 is greater than the largest dimension of conventionalbeamlet 80. In some embodiments, the maximum dimension of compositebeamlets 82 is less than 25 times the size of the largest dimension ofconventional beamlet 80.

The dose simulation computation (e.g. the block 68 dose simulation) isperformed at a number of control points 32. Based on calculations forthose control points 32, an estimated dose distribution is generated fora radiation source 12 that may be continuously moving over a trajectory30 and continuously emitting a radiation beam 14, where the radiationbeam 14 may have a continuously varying shape and intensity. Where adose distribution is computed by summing contributions from discretecontrol points 32, the accuracy with which the computed dose will matchthe actual dose delivered by continuous variation of the position ofsource 12, the orientation of beam 14, the beam shape and the beamintensity will depend in part upon the number of control points 32 usedto perform the dose simulation computation. If there are only a fewcontrol points 32, then it may not be possible to obtain accurateestimates of the delivered dose. The dose delivered by source 12 over acontinuous trajectory 30 can be perfectly modelled by summingcontributions from discrete control points 32 only at the limit wherethe number of control points 32 approaches infinity. Discretization ofthe dose simulation calculation using a finite number of control points32 will therefore degrade the accuracy of the modelled dosedistribution.

This concept is graphically illustrated in FIG. 6, which plots the dosesimulation error against the number of control points 32. FIG. 6 clearlyshows that where the dose simulation computation makes use of a largenumber of control points 32, the resultant error (i.e. the differencebetween simulation dose distribution and actual dose distribution) isminimized.

In some embodiments of the invention, constraints are imposed on theoptimization process (e.g. block 54 of method 50). Such constraints maybe used to help maintain the accuracy of the discretized dose simulationcalculation to within a given tolerance. In some embodiments, theseoptimization constraints are related to the amount of change in one ormore parameters that may be permitted between successive control points32. Examples of suitable constraints include:

-   -   Radiation source 12 cannot travel further than a maximum        distance between consecutive control points 32. This may be        achieved entirely, or in part, by imposing a maximum change in        any motion axis between consecutive control points 32. Separate        constraints may be provided for each motion axis. For example, a        maximum angular change may be specified for gantry angle,        maximum changes in displacement may be provided for couch        translation etc.    -   Parameters affecting beam shape cannot change by more than        specified amounts between consecutive control points 32. For        example, maximum values may be specified for changes in the        positions of leaves 36 of a MLC 35 or changes in MLC orientation        angle φ of MLC 35.    -   Parameters affecting beam shape cannot change by more than a        specified amount per unit of motion axis change. For example,        maximum values may be specified for changes in the positions of        leaves 36 of a MLC 35 for each degree of rotation of gantry 16        about axis 18.    -   The source intensity cannot change by more than a specified        amount between control points 32.    -   The source intensity cannot change by more that a specified        amount per unit of motion axis change.    -   The source intensity cannot exceed a certain level.        It will be appreciated that where a dose simulation calculation        is based on a number of discretized control points, constraints        which force small changes of motion axes parameters, beam shape        parameters and/or beam intensity parameters between control        points can produce more accurate dose simulation calculations.

In addition to improving the accuracy of the dose simulationcalculation, the imposition of constraints may also help to reduce totaltreatment time by accounting for the physical limitations of particularradiation delivery apparatus. For example, if a particular radiationdelivery apparatus has a maximum radiation output rate and theoptimization solution generated by method 50 involves a desiredradiation intensity that results in a radiation output rate higher thanthis maximum radiation output rate, then the rate of movement of themotion axes of the radiation delivery apparatus will have to slow downin order to deliver the intensity prescribed by the block 54optimization process. Accordingly, a constraint imposed on the maximumsource intensity during the block 54 optimization can force a solutionwhere the prescribed intensity is within the capability of the radiationdelivery apparatus (e.g. less than the maximum radiation output rate ofthe radiation delivery apparatus) such that the motion axes of theradiation delivery apparatus do not have to slow down. Since the motionaxes do not have to slow down, such a solution can be delivered tosubject S relatively quickly, causing a corresponding reduction in totaltreatment time. Those skilled in the art will appreciate that otherconstraints may be used to account for other limitations of particularradiation delivery apparatus and can be used to reduce total treatmenttime.

An example of how such constraints may be defined is “For an estimateddose to be within 2% of the actual dose distribution, the followingparameters should not change by more than the stated amounts between anytwo consecutive control points 32:

-   -   intensity—10%;    -   MLC leaf position—5 mm;    -   MLC orientation φ—5%;    -   gantry angle—1 degree; and    -   couch position—3 mm.”

The number of control points 32 used in optimization process 54 alsoimpacts the number of iterations (and the corresponding time) requiredto implement optimization process 54 as well as the quality of the dosedistribution. FIG. 7 graphically depicts the dose distribution qualityas a function of the number of iterations involved in a block 54optimization process for various numbers of control points 32.

FIG. 7 shows plots for 10 control points, 50 control points, 100 controlpoints and 300 control points on a logarithmic scale. It will beappreciated by those skilled in the art that the number of iterations(the abscissa in FIG. 7) is positively correlated with the timeassociated to perform the optimization. FIG. 7 shows that when thenumber of control points 32 is relatively low, the quality of the dosedistribution improves rapidly (i.e. over a relatively small number ofiterations). However, when the number of control points 32 is relativelylow, the quality of the resultant dose distribution is relatively poorand, in the cases of 10 control points and 50 control points, thequality of the dose distribution does not achieve the optimization goals61. Conversely, if a relatively large number of control points 32 isused, the block 54 optimization requires a relatively large number ofiterations, but the quality of the dose distribution eventually achievedis relatively high and exceeds the optimization goals 61. In some cases,where the number of control points 32 is relatively high, the number ofiterations required to achieve a solution that meets the optimizationgoals 61 can be prohibitive (i.e. such a solution can take too long orcan be too computationally expensive).

The impact of the number of control points 32 on the block 54optimization process may be summarized as follows. If a relatively smallnumber of control points 32 are used:

-   -   there may be relatively large changes in the motion axes        parameters (i.e. beam position and beam orientation), the beam        shape parameters (e.g. positions of leaves 36 of MLC 35 and/or        MLC orientation angle φ) and beam intensity between control        points 32 (i.e. the constraints on the motion axes parameters,        the beam shape parameters and the beam intensity will be        relatively relaxed as between control points 32);    -   because of the relatively relaxed constraints and the large        range of permissible changes to the beam shape and intensity        parameters, it is possible to explore a relatively large range        of possible configurations of the beam intensity and beam shape        during optimization process 54;    -   because of the ability to explore a relatively large range of        possible beam shape and intensity configurations, the block 54        optimization process will tend to approach the optimization        goals 61 after a relatively small number of iterations;    -   because there are fewer control points available at which the        beam shape parameters and/or beam intensity parameters may be        varied, it may be difficult or impossible for the block 54        optimization process to derive a dose distribution that meets or        exceeds optimization goals 61; and    -   the accuracy of dose simulation computations based on the        relatively small number of control points 32 will be relatively        poor and may be outside of an acceptable range.

If a relatively large number of control points 32 are used:

-   -   the possible magnitudes of the changes in the motion axes        parameters (i.e. beam position and beam orientation), the beam        shape parameters (e.g. positions of leaves 36 of MLC 35 and/or        MLC orientation angle φ) and beam intensity between control        points 32 are relatively low (i.e. the constraints on the motion        axes parameters, the beam shape parameters and the beam        intensity will be relatively restrictive as between control        points 32);    -   because of the relatively restrictive constraints and the small        range of permissible changes to the beam shape and intensity        parameters, only a relatively small range of possible beam shape        and beam intensity configurations may be explored during        optimization process 54;    -   because of the limited range of possible beam shape and        intensity configurations, it may take a relatively large number        of iterations for the block 54 optimization process to approach        the optimization goals 61;    -   because there are more control points available at which the        beam shape and/or the beam intensity may be varied, it may be        easier to derive a dose distribution that meets or exceeds        optimization goals 61; and    -   the accuracy of dose simulation computations based on the        relatively large number of control points 32 will be relatively        good.

In some embodiments, the benefits of having a small number of controlpoints 32 and the benefits of having a large number of control points 32are achieved by starting the optimization process with a relativelysmall number of control points 32 and then, after a number of initialiterations, inserting additional control points 32 into the optimizationprocess. This process is schematically depicted in FIG. 8.

FIG. 8 shows a method 150 of optimizing dose delivery according toanother embodiment of the invention. Method 150 of FIG. 8 may be used asa part of block 320 in method 300 of FIG. 4B. In many respects, method150 of FIG. 8 is similar to method 50 of FIG. 4A. Method 150 comprises anumber of functional blocks which are similar to those of method 50 andwhich are provided with reference numerals similar to the correspondingblocks of method 50, except that the reference numerals of method 150are proceeded by the numeral “1”. Like method 50, the objective ofmethod 150 is to establish a radiation treatment plan that will delivera desired radiation dose distribution to a target volume in a subject S(to within an acceptable tolerance), while minimizing the dose ofradiation delivered to tissues surrounding the target volume or at leastkeeping the dose delivered to surrounding tissues below an acceptablethreshold. This objective may be achieved by varying: (i) across-sectional shape of radiation beam 14; and (ii) an intensity ofbeam 14, while moving radiation source 12 and/or beam 14 along atrajectory 30 relative to subject S.

The principal difference between method 50 of FIG. 4A and method 150 ofFIG. 8 is that the optimization process 154 of method 150 involves arepetition of the optimization process over a number of levels. Eachlevel is associated with a corresponding number of control points 32 andthe number of control points 32 increases with each successive level. Inthe illustrated embodiment, the total number of levels used to performthe block 154 optimization (or, equivalently, the final number ofcontrol points 32 at the conclusion of the block 154 optimizationprocess) may be determined prior to commencing method 150. For example,the final number of control points 32 may be specified by an operatordepending, for example, on available time requirements, accuracyrequirements and/or dose quality requirements. In other embodiments,depending on termination conditions explained in more detail below, thefinal number of control points 32 may vary for each implementation ofmethod 150.

Method 150 starts in block 152 and proceeds in the same manner as method50 until block 158. In the illustrated embodiment, block 158 differsfrom block 58 in that block 158 involves the additional initializationof a level counter. In other respects, block 158 is similar to block 58of method 50. Initialization of the level counter may set the levelcounter to 1 for example. When the level counter is set to 1, method 150selects a corresponding level 1 number of control points 32 to begin theblock 154 optimization process. The level 1 number of control points 32is preferably a relatively low number of control points. In someembodiments, the level 1 number of control points 32 is in a range of2-50. As discussed in more detail below, the level counter isincremented during the implementation of method 150 and each time thelevel counter is incremented, the corresponding number of control points32 is increased.

Using a number of control points 32 dictated by the level counter,method 150 proceeds with blocks 164 through 174 in a manner similar toblocks 64 through 74 of method 50 discussed above. Block 174 differsfrom block 74 in that block 174 involves an inquiry into the terminationconditions for a particular level of method 150. The terminationconditions for a particular level of method 150 may be similar to thetermination conditions in block 74 of method 50. By way of non-limitingexample, the termination conditions for block 174 may comprise any oneor more of:

-   -   successful achievement of optimization goals 61 to within a        tolerance level which may be particular to the current level;    -   successive iterations not yielding optimization results that        approach optimization goals 61; and    -   operator termination of the optimization process.        Additionally or alternatively, the block 174 termination        conditions may include reaching a maximum number of iterations        of blocks 166 through 174 within a particular level of method        150 regardless of the resultant optimization quality. For        example, the maximum number of iterations for level 1 may be        10⁴. The maximum number iterations may vary for each level. For        example, the maximum number of iterations may increase for each        level in conjunction with a corresponding increase in the number        of control points 32 or may decrease for each level in        conjunction with a corresponding increase in the number of        control points 32.

Additionally or alternatively, the block 174 termination conditions mayinclude reaching a maximum number of successful iterations of blocks 166through 174 within a particular level of method 150 (i.e. iterationswhere method 150 proceeds through the block 172 YES output and the block166 variation is kept in block 173). Again, the maximum number ofsuccessful iterations may vary (increase or decrease) for each level. Insome embodiments, the maximum number of successful iterations within aparticular level decreases as the level (i.e. the number of controlpoints 32) increases. In one particular embodiment, the maximum numberof successful iterations decreases exponentially as the level increases.

If the termination criteria have not been met (block 174 NO output),method 150 loops back to perform another iteration of blocks 166 through174 at the current level. If the termination criteria have been met(block 174 YES output), method 150 proceeds to block 178, where method150 inquires into the general termination conditions for optimizationprocess 154. The general termination conditions of block 178 may besimilar to the termination conditions in block 174, except the block 178termination conditions pertain to optimization process 154 as a wholerather than to a particular level of optimization process 154. By way ofnon-limiting example, the termination conditions for block 178 maycomprise any one or more of:

-   -   successful achievement of optimization goals 61 to within a        tolerance level particular to optimization process 154 as a        whole;    -   successive iterations not yielding optimization results that        approach optimization goals 61; and    -   operator termination of the optimization process.        Additionally or alternatively, the block 178 termination        conditions may include reaching a suitable minimum number of        control points 32. This minimum number of control points may        depend on the number of control points 32 required to ensure        that dose simulation calculations have sufficient accuracy (see        FIG. 6).

The block 178 termination conditions may additionally or alternativelycomprise having minimum threshold level(s) of control points 32 forcorresponding changes in the motion axes parameters, the beam shapeparameters and/or the beam intensity parameter. In one particularexample, the block 178 termination conditions may comprise minimumthreshold level(s) of at least one control point 32 for:

-   -   each intensity change greater than 10%;    -   each MLC leaf position change greater than 5 mm;    -   each MLC orientation change greater than 5°;    -   each gantry angle change greater than 1°; and/or    -   each couch position change greater than −3 mm.

If the block 178 termination criteria have been met (block 178 YESoutput), method 150 proceeds to block 175, where the current beam shapesand intensities are saved as an optimization result. After block 175,method 150 terminates. On the other hand, if the block 178 terminationcriteria have not been met (block 178 NO output), method 150 proceeds toblock 180, where the number of control points 32 is increased.

The addition of new control points 32 in block 180 may occur using awide variety of techniques. In one particular embodiment, new controlpoints 32 are added between pairs of existing control points 32. Inaddition to adding new control points 32, block 180 comprisesinitializing the parameter values associated with the newly addedcontrol points 32. For each newly added control point 32, suchinitialized parameter values may include: motion axes parameters whichspecify the position of source 12 and the orientation of beam 14 (i.e.the set of motion axis positions corresponding to the newly addedcontrol point 32); an initial beam shape parameter (e.g. theconfiguration of the leaves 36 and/or orientation φ of a MLC 35); and aninitial beam intensity parameter.

The motion axes parameters corresponding to each newly added controlpoint 32 may be determined by the previously specified trajectory 30(e.g. by desired trajectory data 62). The initial beam shape parametersand the initial beam intensity parameters corresponding to each newlyadded control point 32 may be determined by interpolating between thecurrent beam shape parameters and current beam intensity parameters forpreviously existing control points 32 on either side of the newly addedcontrol point 32. Such interpolation may comprise linear or non-linearinterpolation for example.

The initial parameter values for the newly added control points 32 andthe subsequent permissible variations of the parameter values for thenewly added control points 32 may be subject to the same types ofconstraints discussed above for the original control points 32. Forexample, the constraints on the parameter values for newly added controlpoints 32 may include:

-   -   constraints on the amount that radiation source 12 (or any one        or more motion axes) can move between control points 32;    -   constraints on the amount that the beam shape can change between        successive control points 32 (e.g. constraints on the maximum        rotation MLC orientation φ or movement of the leaves 36 of MLC        35); or    -   constraints on the amount that the intensity of source 12 may        change between successive control points 32.        Those skilled in the art will appreciate that the magnitude of        these optimization constraints will vary with the number of        control points 32 and/or the separation of adjacent control        points 32. For example, if the constraint on a maximum movement        of a leaf 36 of MLC 35 is 2 cm between successive control points        32 when there are 100 control points 32 and the number of        control points 32 is doubled to 200, the constraint may be        halved, so that the constraint on the maximum movement of a leaf        36 of MLC 35 is 1 cm between control points 32 (assuming that        the newly added control points 32 are located halfway between        the existing control points 32).

After adding and initializing the new control points 32 in block 180,method 180 proceeds to block 182 where the level counter 182 isincremented. Method 150 then returns to block 164, where the iterationprocess of blocks 164 through 174 is repeated for the next level.

An example of the method 150 results are shown in FIG. 9, whichgraphically depicts the dose distribution quality versus the number ofiterations on a linear scale. FIG. 9 also shows that the number ofcontrol points 32 increases as the dose distribution gets closer to theoptimization goals 61. It can be seen that by starting the optimizationprocess with a relatively low number of control points 32 and thenadding additional control points 32 as the optimization processapproaches the optimization goals 61, the number of iterations requiredto achieve an acceptable solution has been dramatically reduced. FIG. 9also shows that:

-   -   the use of a small number of control points 32 at the beginning        of the optimization process allows the optimization to get close        to optimization goals 61 after a relatively small number of        iterations;    -   the introduction of additional control points 32 during the        course of the optimization allows the flexibility to derive a        dose distribution that meets optimization goals 61; and    -   before the overall optimization process is terminated, a large        number of control points 32 have been added and the parameters        associated with these additional control points obey the        associated optimization constraints, thereby preserving the dose        calculation accuracy.

As with method 50 discussed above, method 150 describes a simpleoptimization process 154. In other embodiments, the block 154optimization process may additionally or alternatively include otherknown optimization techniques such as: simulated annealing,gradient-based techniques, genetic algorithms, applying neural networksor the like.

In method 150, additional control points 32 are added when the level isincremented. In a different embodiment, the addition of one or more newcontrol points may be treated as an alteration in block 66 of method 50.In such an embodiment, the procedures of block 180 associated with theaddition of control points 32 may be performed as a part of block 66. Insuch an embodiment, the termination conditions of block 74 may alsocomprise an inquiry into whether the optimization has achieved a minimumnumber of control points 32. In other respects, such an embodiment issimilar to method 50.

The result of optimization method 50 or optimization method 150 is a setof control points 32 and, for each control point 32, a corresponding setof parameters which includes: motion axes parameters (e.g. a set ofmotion axis positions for a particular radiation delivery apparatus thatspecify a corresponding beam position and beam orientation); beam shapeparameters (e.g. a configuration of an MLC 35 including a set ofpositions for leaves 36 and, optionally, an orientation angle φ of MLC35 about axis 37); and a beam intensity parameter. The set of controlpoints 32 and their associated parameters form the basis of a radiationtreatment plan which may then be transferred to a radiation deliveryapparatus to effect the dose delivery.

The radiation intensity at a control point 32 is typically not deliveredinstantaneously to the subject but is delivered continuously throughoutthe portion of the trajectory 30 defined by that control point 32. Theradiation output rate of the source 12 may be adjusted by the radiationdelivery apparatus 10 and control system 23 so that the total radiationoutput for that control point 32 is the same as the intensity determinedfrom the radiation plan. The radiation output rate will normally bedetermined by the amount of time required for the position of theradiation source 12 and the shape of the radiation beam to changebetween the previous, current and following control points 32.

A control system of the radiation delivery apparatus (e.g. controlsystem 23 of radiation delivery apparatus 10) uses the set of controlpoints 32 and their associated parameters to move radiation source 12over a trajectory 30 while delivering radiation dose to a subject S.While the radiation delivery apparatus is moving over trajectory 30, thecontrol system controls the speed and/or position of the motion axes,the shape of the beam and the beam intensity to reflect the motion axisparameters, beam shape parameters and the beam intensity parametersgenerated by the optimization methods 50, 150. It will be appreciated bythose skilled in the art that the output of the optimization methods 50,150 described above may be used on a wide variety of radiation deliveryapparatus.

Pseudocode for Exemplary Embodiment of Optimization Process

Pre-Optimization

-   -   Define 3-dimensional target and healthy tissue structures.    -   Set optimization goals for all structures based on one or more        of:        -   Histograms of cumulative dose;        -   Prescribed dose required to the target;        -   Uniformity of dose to the target;        -   Minimal dose to healthy tissue structures.    -   Combine all optimization goals into a single quality factor        (i.e. an optimization function).    -   Define the trajectory for the radiation source:        -   Select a finite number of control points; and        -   Set the axis position for each axis at each control point.            Initialization    -   Configure MLC characteristics (e.g. leaf width, transmission).    -   Initialize level counter and initial number of control points.    -   Initialize MLC leaf positions to shape the beam to the outline        of the target.    -   Perform dose simulation calculation to simulate dose        distribution for all targets and healthy tissue structures:        -   Generate a random distribution of points in each            target/structure;        -   Calculate the dose contribution from each initial control            point; and        -   Add the contribution from each initial control point.    -   Rescale the beam intensity and corresponding dose so that the        mean dose to the target is the prescription dose.    -   Set constraints for:        -   maximum change in beam shape parameters (i.e. movement of            MLC leaves and/or rotations of MLC); and        -   maximum change in beam intensity;    -   for corresponding variations the relevant motor axes, including,        where relevant:        -   Gantry angle;        -   Couch angle;        -   Couch position; and        -   MLC orientation.    -   Set maximum intensity constraint.    -   Set maximum treatment time constraint.    -   Set optimization parameters:        -   Probability of adding control points;        -   At each iteration:            -   Probability of changing beam shape parameter (e.g. MLC                leaf position or MLC orientation) taking into account                constraints on range of changes in MLC leaf position;                and            -   Probability of changing a radiation intensity taking                into account constraints on range of intensity changes.                Optimization                While the optimization goals have not been attained:

-   1. Select a control point.

-   2. Select a beam shape alteration, intensity alteration, or add    control points.    -   If a beam shape alteration (e.g. a change in position of an MLC        leaf) is selected:        -   Randomly select an MLC leaf to change;        -   Randomly select a new MLC leaf position;        -   Ensure that the new MLC leaf position does not violate any            positional constraints:            -   Leaf does not overlap with opposing leaf;            -   Leaf does not move outside of the initialized aperture;                and            -   Leaf does not violate the maximum movement constraints.        -   Perform dose distribution simulation to calculate the new            dose distribution for all structures.        -   Calculate quality factor (i.e. optimization function) for            new dose distribution.        -   If the quality factor (i.e. optimization function) indicates            an improvement, then accept the new leaf position.    -   If an intensity alteration is selected:        -   Randomly select a new intensity;        -   Ensure that the new intensity does not violate any            constraints:            -   Intensity cannot be negative;            -   Intensity cannot violate the maximum intensity                constraint; and            -   Intensity cannot violate the maximum intensity variation                constraints.        -   Perform dose distribution simulation to calculate the new            dose distribution for all structures.        -   Calculate quality factor (i.e. optimization function) for            new dose distribution.        -   If the quality factor (i.e. optimization function) indicates            an improvement, then accept the new intensity.    -   If adding control points is selected:        -   Insert one or more control points within the existing            trajectory.        -   Adjust optimization constraints (e.g. beam shape constraints            and intensity constraints) based on addition of new control            points.        -   Initialize beam shape parameters, intensity parameters and            motion axes parameters of new control point(s).        -   Perform dose distribution simulation (incorporating the new            control points) to calculate the new dose distribution for            all structures.        -   Rescale all intensities so that the new intensities provide            a mean dose to the target equal to the prescription dose.        -   Continue optimization with the added control points.    -   If the termination criteria have been attained:        -   Terminate the optimization; and        -   Record all optimized parameters (e.g. beam shape parameters,            motion axes parameters and beam intensity parameters) and            transfer optimized parameters to the radiation device.    -   If the termination criteria has not be attained:        -   Go to step (1) and select another beam shape alteration,            intensity alteration, or add control points.

Example Implementation of a Particular Embodiment

The following represents an illustrative example implementation of aparticular embodiment of the invention. FIG. 10 shows athree-dimensional example of target tissue 200 and healthy tissue 202located within the body of a subject S. This example simulates aradiation delivery apparatus similar to radiation delivery apparatus 10(FIG. 1).

In this example, a trajectory 30 is defined as a 360° rotation of gantry16 about axis 18 and a movement of couch 15 in the −Z direction (asshown in the coordinate system of FIG. 10). While this particularexample uses a trajectory 30 involving two motion axes, it will beappreciated that trajectory 30 may involve movement of fewer motion axesor a greater number of motion axes. FIGS. 11A and 11B respectivelydepict the initial control point 32 positions of the relevant motionaxes corresponding to the selected trajectory 30 (i.e. the angularpositions of gantry 16 about axis 18 and the position of couch 15 in theZ dimension).

For this example, the optimization goals 61 included a desired dosedistribution 60 having a uniform level of 70 Gy for target 200 and amaximum dose of 35 Gy for healthy tissue 202. At each initial controlpoint 32, the beam shape parameters were initialized such that theleaves 36 of a MLC 35 shaped the beam into a beam's eye view outline oftarget 200. In this example, the orientation φ of MLC 35 was maintainedconstant at 45° and the orientation φ of MLC 35 was not specificallyoptimized. At each initial control point 32, the beam intensity wasinitialized so that the mean dose delivered to the target 200 was 70 Gy.

FIGS. 12A-F graphically depict the simulated dose distributioncalculation at various stages of the optimization process by way of adose volume histogram (DVH). In FIGS. 12A-F, dashed line 204 representsthe percentage of the volume of healthy tissue 202 that receives acertain quantity of dose and the solid line 206 represents thepercentage of the volume of target 200 that receives a certain quantityof dose. A DVH is a convenient graphical tool for evaluating dosedistribution quality. It will be appreciated that movement of dashedline 204 downwardly and leftwardly represents a minimization of dosedelivered to healthy tissue 202 and that movement of solid line 206upwardly (as far as 100%) and rightwardly (as far as the dosedistribution target (70 Gy in this example)) represents effectivedelivery of dose to target 200.

In this example, the optimization process starts at zero iterations withthe 12 control points depicted in FIGS. 11A and 11B. The result at zeroiterations is shown in FIG. 12A. In this example, the number ofiterations and the number of control points are increased during theoptimization process as shown in FIG. 12B-12F. After 900 iterations andan increase to 23 control points (FIG. 12B), a dramatic improvement indose quality can be observed by the leftwardly and downwardly movementof dashed line 204. Further improvement is seen at 1800 iterations and45 control points (FIG. 12C) and at 3200 iterations and 89 controlpoints (FIG. 12D). The magnitude of the improvement in dose distributionquality per iteration decreases as the optimization progresses. FIGS.12D-12F show that there is little improvement in the dose distributionquality between 3200 iterations and 89 control points (FIG. 12D), 5800iterations and 177 control points (FIG. 12E) and 8500 iterations and 353control points. As discussed above, notwithstanding the minimalimprovement in dose distribution quality between FIGS. 12D and 12F, itcan be useful to continue to increase the number of control points inthe optimization to improve the accuracy of the dose simulationcalculations.

FIG. 13 is another graphical representation of this example which showshow the optimization goals 61 are achieved (to within an acceptabletolerance level) after 5800 iterations (177 control points).

The optimization of this example was terminated after 11,000 iterationsbecause the optimization goals had been attained (to within acceptabletolerances) and there was no further improvement in the dosedistribution quality or accuracy with further iterations. The results ofthis example are shown in FIGS. 14A-14D, which respectively depict themotion axes parameters at each of the final control points (in thiscase, the orientation of gantry 16 about axis 18 (FIG. 14A) and the Zposition of couch 15 (FIG. 14B)), the radiation intensity at each of thefinal control points (FIG. 14C) and the beam shaping parameters at eachof the final control points (in this case, positions of two leaves 36 ofan MLC 35 (FIG. 14D)). FIG. 14D shows that there are no dramatic changesin position of the illustrated leaves 36 of MLC 3, as constraints wereapplied to the allowable rate of change of the leaves 36 of MLC 35.

FIG. 15 shows a two-dimensional cross-section of the optimized dosedistribution. FIG. 15 shows plots contour lines of constant dose(isodose lines) indicating the regions of high and low dose. The amountof dose associated with each isodose line is enumerated on the lineitself. Recalling the shape and relative position of the target 200 andhealthy tissue 202 from FIG. 10, FIG. 15 shows that the high dose regionis confined to the c-shape target area 200 while inside the concavity(i.e. the region of healthy tissue 202), the dose is significantlyreduced.

In this example, the optimization time was 15.3 minutes. The treatmenttime required to deliver this dose distribution is approximately 1.7minutes (assuming a dose rate of 600 MU/min).

In some embodiments, the methods described herein for deliveringradiation dose to a subject S are used in conjunction with one or moreimaging techniques and corresponding imaging apparatus. A suitableimaging technique is cone-beam computed tomography (cone-beam CT), whichobtains a three-dimensional image of a subject. Cone-beam CT involves aradiation source and a corresponding sensor which can be suitablymounted on a radiation delivery apparatus. For example, a cone-beam CTradiation source may be mounted on gantry 16 of radiation deliveryapparatus 10 and a corresponding sensor may be mounted on the opposingside of subject S to detect radiation transmitted through subject S. Insome embodiments, the cone-beam CT source is the same as the treatmentradiation source 12. In other embodiments, the cone-beam CT source isdifferent than the treatment radiation source 12. The radiation deliveryapparatus may move the cone-beam CT source and the CT sensor relative tosubject S using the same motion axes (or substantially similar motionaxes) used to move the treatment radiation source 12. At any point inwhich the cone-beam CT source is activated, a 2-dimensional projectionimage is formed from the transmission of radiation emanating from thecone-beam CT source, passing through subject S and impinging onto thecorresponding sensor (which typically comprises a 2-dimensional array ofradiation sensors). In some embodiments, the cone-beam CT radiationsource and the treatment radiation source are time division multiplexed,such that the cone-beam CT sensor can distinguish between imagingradiation and treatment radiation.

In the acquisition of a 3-dimensional cone-beam CT image, the cone-beamCT source and sensor array move through a trajectory to acquire aplurality of 2-dimensional projection images of subject S. The pluralityof 2-dimensional projection images are combined using methods known tothose skilled in the art in order to reconstruct the 3-dimensional imageof subject S. The 3-dimensional image may contain spatial information ofthe target and healthy tissue.

In some embodiments, a cone-beam CT image of subject S is acquired whiledelivering radiation to the subject. The 2-dimensional images may betaken from around the same trajectory 30 and in the same time intervalthat the radiation is delivered to subject S. In such embodiments, theresultant cone-beam CT image will be representative of the subjectposition, including the 3-dimensional spatial distribution of target andhealthy tissue, at the time the subject was treated. The spatialdistribution of target and healthy tissue can be referenced to theparticular radiation delivery apparatus, allowing an observer toaccurately assess what radiation dose distribution was actuallydelivered to the target and healthy tissue structures.

Subject S, and more particularly, the locations of target and healthytissue, can move during radiation delivery. While some movement can bereduced or eliminated, one difficult movement to stop is respiration.For example, when subject S breathes, a target located inside the lungmay shift as a function of the breathing cycle. In most dose simulationcalculations, subject S is assumed to be stationary throughout thedelivery. Accordingly, ordinary breathing by subject S can result inincorrect delivery of dose to the target and healthy tissue. In someembodiments, radiation source 12 is activated only when a position orconfiguration of subject S is within a specified range.

In some embodiments, one or more sensors are used to monitor theposition of subject S. By way of non-limiting example, such sensors mayinclude respirometer, infrared position sensors, electromyogram (EMG)sensors or the like. When the sensor(s) indicate that subject S is in anacceptable position range, radiation source 12 is activated, theconfiguration of beam-shaping mechanism 33 changes and the motion axesmove as described in the radiation treatment plan. When the sensor(s)indicate that subject S is not in the acceptable position range, theradiation is deactivated, the configuration of beam-shaping mechanism 33is fixed and the motion axes are stationary. An acceptable positionrange may be defined as a particular portion of the respiratory cycle ofsubject S. In such embodiments, the radiation treatment plan isdelivered intermittently, with intervals where the radiation apparatusand radiation output are paused (i.e. when the subject is out of theacceptable position range) and intervals where the radiation apparatusand radiation output are resumed (i.e. when the subject is in theacceptable position range). Treatment delivery proceeds in this wayuntil the treatment plan has been completely delivered. The process ofposition dependent delivery of radiation may be referred to as “positiongating” of radiation delivery.

In one particular embodiment of the invention, cone-beam CT images areacquired while position gated treatment is being delivered to subject S.The acquisition of 2-dimensional projection images may also gated to thepatient position, so that the cone-beam CT images will represent theposition of subject S at the time of treatment delivery. Suchembodiments have the additional benefit that the 2-dimensional cone-beamCT images are obtained with subject S in a consistent spatial position,thereby providing a 3-dimensional cone-beam CT with fewer motionartifacts.

As discussed above, in some embodiments where beam-shaping mechanism 33comprises a MLC 35, it is possible to optimize beam-shape parametersincluding, without limitation: the positions of MLC leaves 36 and thecorresponding shape of the MLC apertures 38; and the MLC orientationangle φ about beam axis 37. In other embodiments where beam-shapingmechanism 33 comprises a MLC 35, it may be desired to maintain aconstant MLC orientation angle φ about beam axis 37—e.g. where aparticular radiation delivery apparatus 10 does not permit adjustment ofMLC orientation angle φ during delivery and/or where processing powerused in the optimization process is at a premium.

Where MLC orientation angle φ is maintained constant, MLC 35 may havecertain limitations in its ability to approximate arbitrary beam shapes.In such instances, the selection of the particular constant MLCorientation angle φ may impact treatment plan quality and ultimately theradiation dose that is delivered to subject S. An example of thisscenario is illustrated schematically in FIGS. 16A and 16B, where it isdesired to provide a beam shape 301. In the FIG. 16A example,leaf-translation directions 41 are oriented substantially parallel withthe motion of beam 14 along source trajectory direction 43 (i.e. MLCorientation angle φ=0°). In the FIG. 16B example, leaf-translationdirections 41 are oriented substantially orthogonally to the motion ofbeam 14 along source trajectory direction 43 (i.e. MLC orientation angleφ=90°). It can be seen by comparing FIGS. 16A and 16B that when φ=0°(FIG. 16A), the beam shape of MLC 35 does a relatively good job ofapproximating desired beam shape 301, whereas when φ=90° (FIG. 16B),there are regions 303 where MLC does a relatively poor job ofapproximating desired beam shape 301.

While not explicitly shown in FIGS. 16A and 16B, it will be understoodthat if desired beam shape 301 was rotated 90° from the orientationshown in FIGS. 16A and 16B, then the MLC orientation angle φ=90° wouldproduce a relatively accurate beam shape relative to the MLC orientationof φ=0°. Accordingly, selection of a constant MLC orientation φ mayimpact treatment plan quality and ultimately the radiation dose that isdelivered to subject S. It is therefore important to consider which MLCorientation angle φ to select when using a constant MLC orientationangle φ to plan and deliver radiation to a subject S.

One aspect of the invention provides for radiation planning and deliverysystems which provide a constant MLC orientation angle φ having agenerally preferred value. Consider a trajectory 30 where radiation isdelivered to a subject where there are substantially opposing radiationbeams throughout the trajectory—i.e. beams that are parallel but haveopposing directions. By way of non-limiting example, such a trajectory30 may comprise rotation of gantry 16 through one full rotation of 180°or more. FIG. 17 shows the projections 305A, 305B of target 307 andhealthy tissue 309 for opposing beam directions (e.g. a gantry angle of0° (305A) and a gantry angle of 180° (305B). The projection of target307 and healthy tissue 309 is approximately mirrored for the paralleland opposing beam directions.

It follows that a desirable beam shape from parallel and opposing beamdirections will also be approximately mirrored. This observed symmetrymay be exploited by selecting MLC orientation angles φ that result in asuperior radiation plan. Consider the examples shown in FIGS. 16A and16B with respect to the shaping capabilities of MLC 35. For the twoorientations shown in FIG. 16A (φ=0°) and in FIG. 16B (φ=90°), mirroringthe desired beam shape will not change the ability of MLC 35 to createdesired beam shape 301 because of the mirror symmetry already inherentin MLC 35. In particular embodiments, MLC orientation angle φ isselected to exploit the mirroring of opposing beam projections bychoosing an MLC orientation φ that is not 0° or 90° such that the MLCorientations φ with respect to the subject S will be different foropposing beam directions.

For example, choosing MLC orientation angle φ such that |φ|=45° (where|•| represents an absolute value operator) will result in MLCorientations that are orthogonal to one another (i.e. with respect tothe projection of subject S) when the beam is oriented in opposing beamdirections. This is shown in FIGS. 18A and 18B which show MLC 35 and theprojections of target 307 and healthy tissue 309 for opposing beamdirections corresponding to opposing gantry angles of 0° (FIG. 18A) and180° (FIG. 18B) and in FIGS. 18C and 18D which show MLC 35 and theprojections of desired beam shape 301 for opposing beam directionscorresponding to opposing gantry angles of 0° (FIG. 18C) and 180° (FIG.18D). With this MLC orientation angle |φ|=45°, the beam shapinglimitations associated with constant MLC orientation angle φ are therebyreduced because effectively two different MLC orientation angles φ areavailable for opposing beam directions and may be used to provide adesired beam shape.

Other MLC orientation angles φ that are not φ=0° or φ=90° may alsoprovide this advantage. Currently preferred embodiments incorporate MLCorientation angles φ such that |φ| is in a range 15°-75° andparticularly preferred embodiments incorporate MLC angles φ where |φ| isin a range of 30°-60°. The benefit of selecting MLC orientation angles φwithin these ranges may be realized for all substantially opposed beamorientations throughout the delivery of radiation and may be provided byany trajectories which comprise one or more substantially opposed beamdirections. Non-limiting examples of such trajectories 30 include:trajectories 30 which comprise rotations of gantry 16 about axis 18 byany amount greater than 180° (e.g. 360° rotations of gantry 16 aboutaxis 18) and trajectories 30 which comprise multiple planar arcs whereinat least one of the arcs comprises opposing beam directions. Selectionof MLC orientation angles φ within these ranges is not limited totrajectories 30 comprising opposing beams and may be used for anytrajectories. These advantages of increased MLC shaping flexibility maybe manifested as increased plan quality, reduced delivery time, reducedradiation beam output requirements or any combination of the above.

A further desirable aspect of providing MLC orientation angles φ thatare not φ=0° or φ=90° relates to physical properties of typical MLCs 35.Although individual MLC leaves 36 block most of radiation from radiationsource 12, there is often some undesirable radiation leakage thatpermeates MLC 35 and there is a relatively large amount of radiationleakage at the edges of MLC leaves 36 where they translate independentlyrelative to each other. Choosing a MLC orientation angle φ=0° withrespect to the motion of beam 14 along source trajectory direction 43may result in interleaf radiation leakage that is compounded in planesdefined by the edges of MLC leaves 36 and the beam axis 37 forparticular trajectories 30. MLC orientation angles φ other than φ=0° maycause the orientation of the interleaf leakage planes to change alongthe trajectory 30, thereby reducing any systematic accumulation ofunwanted radiation leakage and corresponding unwanted dose withinsubject S.

The edges of MLC leaves 36 may be constructed with a tongue-and-grooveshape on each side for reduction of interleaf leakage. For some beamshapes, such tongue-and-groove MLC leaf edges may cause an unwantedreduction in radiation dose delivered to subject S. Similar to theeffect on inter-leaf leakage, the tongue-and-groove underdosage effectwill be compounded along the leaf edges. Selecting MLC orientationangles φ other than φ=0° may reduce systematic underdosing of thesubject S caused by these tongue-and-groove leaf edges.

Additional considerations that affect the selection of MLC orientationangle φ include the maximum speed of MLC leaves 36 as well as theability of MLC 35 to create shapes that continuously block areas ofimportant healthy tissue 309 while maintaining a relatively high dose totarget 307.

When it is desirable to block a central portion of radiation beam 14, itcan be more efficient to choose a MLC orientation angle φ other thanφ=0°. In particular circumstances, blocking a central portion ofradiation beam 14 may be achieved more efficiently when the MLCorientation angle φ is approximately φ=90°. In contrast, when there aredramatic changes in desired beam shape as source 12 moves along itstrajectory 30, it may be difficult for MLC leaves 36 to move intoposition with sufficient speed. Generally, the desired projection shape301 will change more rapidly in the direction 43 of source motion alongtrajectory 30. It may therefore be desirable to have leaf-translationaxis 41 oriented to approximately the same direction 43 as the sourcemotion along trajectory 30. Such a selection would result in a MLCorientation angle φ of approximately φ=0°.

The competing benefits/disadvantages of a MLC orientation angle φ of 0°versus 90° may be mitigated by using a MLC orientation angle φ that issubstantially in between these two angles (i.e. approximately |φ|=45°.It will be appreciated that a MLC orientation angle φ of exactly |φ|=45°is not essential and other factors specific to the given subject S to beirradiated may need to be considered when selecting a MLC orientationangle φ.

Certain implementations of the invention comprise computer processorswhich execute software instructions which cause the processors toperform a method of the invention. For example, one or more dataprocessors may implement the methods of FIG. 4A and/or FIG. 8 byexecuting software instructions in a program memory accessible to thedata processors. The invention may also be provided in the form of aprogram product. The program product may comprise any medium whichcarries a set of computer-readable signals comprising instructionswhich, when executed by a data processor, cause the data processor toexecute a method of the invention. Program products according to theinvention may be in any of a wide variety of forms. The program productmay comprise, for example: physical media such as magnetic data storagemedia including floppy diskettes, hard disk drives, optical data storagemedia including CD ROMs, DVDs, electronic data storage media includingROMs, flash RAM, or the like. The computer-readable signals on theprogram product may optionally be compressed or encrypted.

Where a component (e.g. a software module, processor, assembly, device,circuit, etc.) is referred to above, unless otherwise indicated,reference to that component (including a reference to a “means”) shouldbe interpreted as including as equivalents of that component anycomponent which performs the function of the described component (i.e.,that is functionally equivalent), including components which are notstructurally equivalent to the disclosed structure which performs thefunction in the illustrated exemplary embodiments of the invention.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize certain modifications,permutations, additions and sub-combinations thereof. For example:

-   -   In the embodiments described above, control points 32 used to        define a trajectory 30 are the same as the control points used        to perform the block 54 optimization process. This is not        necessary. For example, a simple trajectory 30, such as an arc        of gantry 16 about axis 18 (FIG. 1), may be defined by two        control points at its ends. While such control points may define        the trajectory, more control points will generally be required        to achieve an acceptable treatment plan. Accordingly, the block        54, 154 optimization processes may involve using different (e.g.        more) control points than those used to define the trajectory.    -   In the embodiments described above, constraints (e.g.        constraints on the changes in beam position/orientation        parameters between control points 32, constraints on the changes        in beam shape parameters between control points 32 and        constraints on the changes in the beam intensity between control        points 32) are used throughout the optimization processes 54,        154. In other embodiments, the optimization constraints may be        imposed later in the optimization process. In this manner, more        flexibility is available in meeting the optimization goals 61 in        an initial number of iterations. After the initial number of        iterations is performed, the constraints may be introduced. The        introduction of constraints may require that some beam        position/orientation parameters, beam shape parameters and/or        intensity parameters be changed, which may result in a need for        further optimization to meet the optimization goals 61.    -   In the embodiments described above, the beam position and beam        orientation at each control point 32 are determined prior to        commencing the optimization process 54, 154 (e.g. in blocks 52,        152) and are maintained constant throughout the optimization        process 54, 154 (i.e. optimization processes 54, 154 involve        varying and optimizing beam shape parameters and beam intensity        parameters, while trajectory 30 remains constant). In other        embodiments, the beam position and beam orientation parameters        (i.e. the set of motion axis positions at each control point 32)        are additionally or alternatively varied and optimized as a part        of optimization processes 54, 154, such that optimization        processes 54, 154 optimize the trajectory 30 of the radiation        delivery apparatus. In such embodiments, optimization processes        54, 154 may involve placing constraints on the available motion        axis positions and/or the rate of change of motion axis        positions between control points 32 and such constraints may be        related to the physical limitations of the particular radiation        delivery apparatus being used to deliver the dose to the subject        S.    -   In some embodiments, the radiation intensity may be held        constant and the optimization processes 54, 154 optimize the        beam shape parameters and/or the motion axis parameters. Such        embodiments are suitable for use in conjunction with radiation        delivery apparatus which do not have the ability to controllably        vary the radiation intensity. In some embodiments, the beam        shape parameters may be held constant and the optimization        processes 54, 154 optimize the intensity and/or the motion axis        parameters.    -   There are an infinite number of possible trajectories that can        be used to describe the position and orientation of a radiation        beam. Selection of such trajectories are limited only by the        constraints of particular radiation delivery apparatus. It is        possible to implement the invention using any trajectory capable        of being provided by any suitable radiation delivery apparatus.    -   It is therefore intended that the following appended claims and        claims hereafter introduced are interpreted to include all such        modifications, permutations, additions and sub-combinations as        are within their true spirit and scope.

What is claimed is:
 1. A method for planning delivery of radiation doseto a target region within a subject, the method comprising: iterativelyoptimizing, by a processor, a simulated dose distribution relative to aset of one or more optimization goals comprising a desired dosedistribution in the subject over an initial plurality of control pointsalong a trajectory which involves relative movement between a radiationsource and the subject; reaching one or more initial terminationconditions and, after reaching the one or more initial terminationconditions: specifying, by the processor, an increased plurality ofcontrol points along the trajectory, the increased plurality of controlpoints comprising a larger number of control points than the initialplurality of control points; and iteratively optimizing, by theprocessor, a simulated dose distribution relative to the set of one ormore optimization goals over the increased plurality of control pointsto thereby determine a radiation delivery plan; the radiation deliveryplan configured to cause a radiation delivery apparatus to deliverradiation in accordance with the radiation delivery plan.
 2. A methodaccording to claim 1 wherein iteratively optimizing, by the processor,the simulated dose distribution relative to the set of one or moreoptimization goals over the initial plurality of control pointscomprises iteratively optimizing, by the processor, the simulated dosedistribution subject to one or more initial optimization constraints. 3.A method according to claim 2 wherein iteratively optimizing, by theprocessor, the simulated dose distribution relative to the set of one ormore optimization goals over the increased plurality of control pointscomprises iteratively optimizing, by the processor, the simulated dosedistribution subject to one or more subsequent optimization constraints.4. A method according to claim 3 wherein at least one of the subsequentoptimization constraints is different than a corresponding at least oneof the initial optimization constraints.
 5. A method according to claim1 comprising discontinuing the iterative optimization over the increasedplurality of control points after reaching one or more subsequenttermination conditions.
 6. A method according to claim 5 wherein: priorto reaching the one or more initial termination conditions, a differencebetween the simulated dose distribution and the set of one or moreoptimization goals is greater than an acceptable dose quality threshold;and before discontinuing the iterative optimization over the increasedplurality of control points, the difference between the simulated dosedistribution and the set of one or more optimization goals is within theacceptable dose quality threshold.
 7. A method according to claim 1wherein iteratively optimizing, by the processor the simulated dosedistribution relative to the set of one or more optimization goals overthe initial plurality of control points comprises achieving an initialdose simulation accuracy and wherein iteratively optimizing, by theprocessor, the simulated dose distribution relative to the set of one ormore optimization goals over the increased plurality of control pointscomprises achieving an increased dose simulation accuracy, the increaseddose simulation accuracy more accurately representing actual dosedistribution in the subject than the initial dose simulation accuracy.8. A method according to claim 1 wherein iteratively optimizing, by theprocessor, the simulated dose distribution relative to the set of one ormore optimization goals over the increased plurality of control pointscomprises, for each iteration: varying, by the processor, one or moreradiation delivery parameters associated with one or more of theincreased plurality of control points; determining, by the processor thesimulated dose distribution based on the one or more varied radiationdelivery parameters; determining, by the processor and on the basis ofan optimization algorithm and the simulated dose distribution based onthe one or more varied radiation delivery parameters, whether to acceptor reject the one or more varied radiation delivery parameters; andafter a determination is made to accept the one or more varied radiationdelivery parameters, updating, by the processor, current radiationdelivery parameters to include the one or more varied radiation deliveryparameters.
 9. A method according to claim 8 wherein the one or moreradiation delivery parameters comprise one or more configurations of amulti-leaf collimator.
 10. A method according to claim 9 wherein the oneor more configurations of the multi-leaf collimator comprise positionsof one or more leaves of the multi-leaf collimator.
 11. A methodaccording to claim 10 wherein the trajectory involves relative movementbetween the radiation source and the subject in a source trajectorydirection, wherein the one or more leaves of the multi-leaf collimatorare moveable in a leaf-translation direction and wherein, duringrelative movement between the radiation source and the subject along thetrajectory, the leaf-translation direction is oriented at a MLCorientation angle with respect to the source trajectory direction, anabsolute value of the MLC orientation angle satisfying 0°<|φ|<90°.
 12. Amethod according to claim 11 wherein the absolute value of the MLCorientation angle satisfies 30°≦|φ|≦60°.
 13. A method according to claim10 wherein the trajectory involves relative movement between theradiation source and the subject in a source trajectory direction,wherein the one or more leaves of the multi-leaf collimator are moveablein a leaf-translation direction and wherein, during relative movementbetween the radiation source and the subject along the trajectory, theleaf-translation direction is oriented at a MLC orientation angle withrespect to the source trajectory direction, the MLC orientation angleconstant throughout the trajectory.
 14. A method according to claim 8wherein the one or more radiation delivery parameters comprise anintensity of the radiation source for a plurality of the increasedplurality of control points.
 15. A method according to claim 1 whereiniteratively optimizing, by the processor, the simulated dosedistribution relative to the set of one or more optimization goals overthe initial plurality of control points comprises modeling, by theprocessor, a dose contribution for at least one of the initial pluralityof control points by: dividing a cross-sectional area of a beam into aplurality of two-dimensional beamlets; and simulating a dosedistribution contribution from each of the plurality of two-dimensionalbeamlets.
 16. A method according to claim 15 wherein iterativelyoptimizing, by the processor, the simulated dose distribution relativeto the set of one or more optimization goals over the initial pluralityof control points comprises mapping, by the processor, between theplurality of two dimensional beamlets and one or more of: a position ofthe radiation source and orientation of a beam from the radiation sourcerelative to the subject; a beam shape involving a configuration of oneor more leaves of a multi-leaf collimator; and an intensity of theradiation source.
 17. A method according to claim 1 wherein iteratively,by the processor, optimizing the simulated dose distribution relative tothe set of one or more optimization goals over the initial plurality ofcontrol points comprises performing, by the processor, the iterativeoptimization using a set of optimization parameters, the set ofoptimization parameters representative of one or more of: a beam shapeof the radiation source; and a beam intensity of the radiation source.18. A method according to claim 17 wherein specifying, by the processor,an increased plurality of control points comprises assigning, by theprocessor, optimization parameters to the increased plurality of controlpoints not present among the initial plurality of control points,wherein assigning, by the processor, optimization parameters comprisesinterpolating, by the processor, optimization parameters based on theoptimization parameters associated with the initial plurality of controlpoints.
 19. A method according to claim 1 wherein the trajectorycomprises at least one pair of locations where a first beam directedfrom the radiation source toward the subject from a first one of thepair of locations and a second beam directed from the radiation sourcetoward the subject from a second one of the pair of locations aresubstantially parallel but opposing one another.
 20. A method accordingto claim 1 wherein the trajectory comprises a plurality of arcs, eacharc involving relating movement between the radiation source and thesubject within a corresponding plane.
 21. A method according to claim 20wherein between successive ones of the plurality of arcs, the trajectorycomprises inter-arc relative movement between the radiation source andthe subject, the inter-arc relative movement comprising movement suchthat the corresponding planes associated with each arc intersect oneanother.
 22. A method according to claim 20 wherein between successiveones of the plurality of arcs, the trajectory comprises inter-arcrelative movement between the radiation source and the subject, theinter-arc relative movement comprising movement such that thecorresponding planes associated with each arc are parallel to oneanother.
 23. A method according to claim 1 wherein the trajectorycomprises a non-self overlapping trajectory which involves non-selfoverlapping relative movement between the radiation source and thesubject.
 24. A method according to claim 1 wherein a start of thetrajectory and an end of the trajectory comprise the same relativeposition between the radiation source and the subject and the trajectoryis otherwise non-self overlapping.
 25. A method according to claim 1comprising at least one of: defining the set of one or more optimizationgoals; and receiving the set of one or more optimization goals from anexternal source.
 26. A method according to claim 1 comprising providingthe radiation delivery plan to the radiation delivery apparatus.
 27. Amethod according to claim 1 comprising delivering, by the radiationdelivery apparatus, radiation in accordance with the radiation deliveryplan.
 28. A program product comprising a non-transitorycomputer-readable medium comprising computer-readable instructionswhich, when executed by a processor, cause the processor to perform themethod of claim
 1. 29. A program product comprising a non-transitorycomputer-readable medium comprising computer readable instructionswhich, when executed by a processor, cause the processor to execute amethod for planning delivery of radiation dose to a target region withina subject, the method comprising: iteratively optimizing a simulateddose distribution relative to a set of one or more optimization goalscomprising a desired dose distribution in the subject over an initialplurality of control points along a trajectory which involves relativemovement between a radiation source and the subject; reaching one ormore initial termination conditions and, after reaching the one or moreinitial termination conditions: specifying an increased plurality ofcontrol points along the trajectory, the increased plurality of controlpoints comprising a larger number of control points than the initialplurality of control points; and iteratively optimizing a simulated dosedistribution relative to the set of one or more optimization goals overthe increased plurality of control points.